WO2021238826A1 - Procédé et appareil de formation d'un modèle de segmentation d'instance, et procédé de segmentation d'instance - Google Patents

Procédé et appareil de formation d'un modèle de segmentation d'instance, et procédé de segmentation d'instance Download PDF

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Publication number
WO2021238826A1
WO2021238826A1 PCT/CN2021/095363 CN2021095363W WO2021238826A1 WO 2021238826 A1 WO2021238826 A1 WO 2021238826A1 CN 2021095363 W CN2021095363 W CN 2021095363W WO 2021238826 A1 WO2021238826 A1 WO 2021238826A1
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instance segmentation
model
training
training set
detection frame
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PCT/CN2021/095363
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Chinese (zh)
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荆伟
卢运西
徐兆坤
黄银君
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苏宁易购集团股份有限公司
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/11—Region-based segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/04—Architecture, e.g. interconnection topology
    • G06N3/045—Combinations of networks
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/082—Learning methods modifying the architecture, e.g. adding, deleting or silencing nodes or connections
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/10—Segmentation; Edge detection
    • G06T7/194—Segmentation; Edge detection involving foreground-background segmentation
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10016—Video; Image sequence
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/10—Image acquisition modality
    • G06T2207/10024—Color image

Definitions

  • the present invention belongs to the field of target detection, and particularly relates to a training method, device, and instance segmentation method of an instance segmentation model.
  • the present invention proposes a training method, device, and instance segmentation method for an instance segmentation model.
  • This application prunes the network structure of the existing instance segmentation model to make the entire model more lightweight.
  • the training speed of the model and the prediction speed of the model are improved.
  • a depth map is added, the number of channels is expanded, and the training accuracy and prediction accuracy of the model are improved.
  • the first aspect discloses a method for training an instance segmentation model, and the method includes:
  • the training set being a collection of RGBD images with target objects in a scene collected by different depth cameras, the RGBD images including a depth map and a color map;
  • the method further includes: preprocessing the training set before labeling, which specifically includes:
  • the color map in the training set is normalized.
  • labeling the training set specifically includes:
  • using the labeled training set to train the pruned deep learning model to obtain the instance segmentation model specifically includes:
  • the total loss value is judged, and when the total loss value is less than the second preset value, the training of the deep learning model is stopped and the corresponding deep learning when the total loss value is less than the second preset value
  • the model is determined to be the instance segmentation model.
  • performing regression processing on the characteristic region specifically includes:
  • the method also includes:
  • each generated detection frame is subjected to maximum pooling processing and the detection frame after the maximum pooling processing that meets the first preset condition is stored.
  • using the labeled training set to train the pruned deep learning model specifically includes:
  • pruning the pre-built deep learning model specifically includes:
  • the network layer corresponding to the impact factor is pruned.
  • an instance segmentation method is disclosed, and the method includes:
  • the pre-trained instance segmentation model is obtained by training based on the method described in the first aspect.
  • the method before inputting the image to be detected into a pre-trained instance segmentation model for recognition, the method further includes:
  • Said inputting the picture to be detected into a pre-trained instance segmentation model for recognition, and outputting the detection frame and instance segmentation result of the picture to be detected specifically includes:
  • the method also includes:
  • a training device for an instance segmentation model includes:
  • the pruning module is used to prun the pre-built deep learning model
  • An acquisition module for acquiring a training set is a collection of RGBD images with target objects in a scene collected by different depth cameras, the RGBD images including a depth map and a color map;
  • the training module is used to train the pruned deep learning model using the labeled training set to obtain an instance segmentation model.
  • the present invention makes the network structure lighter by pruning the deep learning model, and the training of the model and the prediction using the model are fast. At the same time, the depth map is added when training the deep learning network, thus expanding the number of channels , To improve the training accuracy, thereby also improving the prediction accuracy;
  • the present invention performs truncation processing and normalization on the depth map in the training data, and normalizes the color map in the training data, which improves the accuracy of the training data, thereby Improve the training accuracy of the model;
  • the present invention uses a special labeling strategy to label the training data, and eliminates data with low integrity, which improves the effectiveness of training data labeling and also improves the training accuracy of the model;
  • the instance segmentation model of the present invention uses the anchor free method to predict the center point of the target object, and then regresses to obtain the width and height, thereby obtaining the detection frame, and performing the maximum pooling process on the detection frame to achieve deduplication, which improves the density of personnel Robustness of the detection under the situation, effectively avoiding the loss of the detection frame under the crowded situation;
  • the present invention realizes parallel processing by splicing the input data and splitting the output result at the same time, which improves the execution efficiency, improves the efficient use of computing resources, and is more in line with video surveillance related Application scenarios.
  • FIG. 1 is a flowchart of a method for training an instance segmentation model provided in Embodiment 1 of the present application;
  • FIG. 2 is a structural diagram of an instance segmentation model provided by Embodiment 1 of the present application.
  • FIG. 3 is a flowchart of an example segmentation method provided in Embodiment 2 of the present application.
  • FIG. 4 is a schematic structural diagram of a training device for an example segmentation model provided in Embodiment 3 of the present application.
  • a training method of an instance segmentation model includes the following steps:
  • This application constructs a basic network structure based on the YOLACT model and constructs a deep learning model.
  • the deep learning model includes: convolutional layer, activation layer, pooling layer, fully connected layer, etc.
  • the specific structure of the model is shown in Figure 2, including the resent18 network, the FPN network, and the two network branches connected to the FPN network (protonet and Pred_heads), crop network, etc.
  • the resent18 network is used to extract features
  • the FPN network is used to fuse the features
  • the protonet network branch is used to segment the feature map to obtain the segmentation results including the foreground and background
  • Pred_heads is used to predict the feature map to get about The detection frame, category, confidence of the target object, and the instance segmentation score associated with the prediction result of the protonet network branch.
  • the network layer is pruned through a coarse-grained method, and the specific steps are as follows:
  • the impact factor is the scaling factor obtained after the normalization calculation of the network layer to be pruned;
  • the aforementioned network layer to be pruned is a convolutional layer.
  • the network layer corresponding to the impact factor is pruned.
  • the normalization layer performs normalization calculations on each convolutional layer.
  • the calculation formula of the normalization layer includes a parameter ⁇ , which is a scaling factor.
  • ⁇ is less than a preset value, the corresponding channel is not important, so the network layer can be pruned.
  • a regular term about ⁇ can be added to the calculation formula, so that automatic pruning can be realized during the training process of the model.
  • the training set is a collection of RGBD images with target objects in a scene collected by different depth cameras.
  • the RGBD images include a depth map and a color map;
  • the purpose of labeling is to preprocess the RGBD image, so that the target object detection frame and label can be obtained.
  • the above-mentioned preset value may be 1/2. Therefore, when the integrity of the target object is greater than 1/2, the target object in the RGBD image with the target object is labeled and the corresponding label is generated.
  • the effectiveness of training data can be improved, and thus the accuracy of subsequent model training and prediction can also be improved.
  • the training set can be further processed, including:
  • a three-dimensional model including the target object and a three-dimensional model not including any target object are constructed.
  • the cutoff distance corresponding to each depth camera determines the cutoff distance corresponding to each depth camera, and the cutoff distance is the active distance from the target object to the depth camera;
  • the cutoff distance can be a dynamic range.
  • Using the cutoff distance to cut off the depth map can filter some noise in the depth map.
  • the accuracy of the training data is improved, thereby improving the training accuracy of the model.
  • Step S14 specifically includes:
  • S141 Perform feature extraction on the annotated training set, and fuse the extracted features to obtain a feature region;
  • the annotated training set is input into the resent18 network.
  • the resent18 network includes several convolutional layers for extracting features of the training set to obtain features of multiple dimensions. After the feature extraction is completed, the features of multiple dimensions are input to the FPN network to obtain the feature area.
  • the FPN network is a feature pyramid network, which can fuse two types of features, solve multi-scale problems, and improve target detection performance.
  • the protonet network branch is used to segment the characteristic area to obtain the segmentation result including the foreground and background.
  • Pred_heads is used To predict the feature area, get the detection frame, category, confidence of the target object and the instance score associated with the segmentation result.
  • S145 Calculate a total loss value according to the error between the truncated instance segmentation result and the corresponding label, and the error between the detection frame and the corresponding target object detection frame;
  • the total loss value is the sum of the error between the truncated instance segmentation result and the corresponding label, and the error between the detection frame and the corresponding target object detection frame.
  • the total loss value is less than a preset value, it indicates that the entire model has converged, and training can be stopped at this time.
  • the gradient descent algorithm is used to optimize the training of the deep learning model.
  • the corresponding learning rate can be set for the loss value of different stages.
  • the deep learning model is trained according to the learning rate corresponding to the current loss value.
  • the model After the model is trained, the model can be verified to ensure the prediction accuracy of the model. Specifically, the following implementation steps can be included:
  • the verification set is a collection of RGBD images with target objects collected by different depth cameras.
  • the RGBD images include depth maps and color maps;
  • the output result can be output in fixed rounds, for example, when the model is iterated every 5 times, the result will be output once, so as to ensure the reasonableness and efficiency of the model verification process.
  • an embodiment of the present invention also provides an instance segmentation method. As shown in FIG. 3, the method includes:
  • the recognition process of the picture to be detected can refer to the training process of the model in Embodiment 1 for details.
  • the confidence level needs to be compared with the preset value. Refer to Figure 2 for details. After the Crop module compares the confidence level with the preset value, the output is higher than the preset value. The detection frame corresponding to the confidence level of the value and the corresponding instance segmentation result.
  • the pre-trained instance segmentation model is obtained by training based on the method described in Embodiment 1.
  • this solution also includes:
  • S43 Input the spliced image to be detected into the instance segmentation model for recognition, and output the detection frame and instance segmentation result of all the images to be detected;
  • the pictures to be detected are spliced before prediction, and the prediction result is split after prediction
  • many pictures can be predicted at the same time, which greatly improves the parallelization ability of model prediction and improves the efficiency of computing resources. Utilization, more in line with the application scenarios related to video surveillance.
  • the embodiment of the present invention also provides a training device for an instance segmentation model. As shown in FIG. 4, the device includes:
  • the pruning module 41 is used for pruning the pre-built deep learning model
  • the acquiring module 42 is used to acquire a training set;
  • the training set is a collection of RGBD images with target objects in a scene collected by different depth cameras, and the RGBD images include a depth map and a color map;
  • the preprocessing module 43 is used to label the training set
  • the training module 44 is used to train the pruned deep learning model using the labeled training set to obtain an instance segmentation model.
  • the preprocessing module 43 is also used to preprocess the training set before labeling, which specifically includes:
  • the cutoff distance corresponding to each depth camera determines the cutoff distance corresponding to each depth camera, and the cutoff distance is the active distance from the target object to the depth camera;
  • the depth map in the training set is truncated, and the truncated depth map is normalized;
  • preprocessing module 43 is specifically used for:
  • training module 44 specifically includes:
  • the feature extraction and fusion module 441 is used to perform feature extraction on the labeled training set, and fuse the extracted features to obtain a feature region;
  • the prediction module 442 is used to perform segmentation processing on the feature area to obtain the segmentation result of the feature area, and perform regression and classification processing on the feature area at the same time, to obtain the detection frame of the feature area, the classification result corresponding to the detection frame, and the correlation with the segmentation result The instance score of the association;
  • the processing module 443 is configured to multiply the segmentation result and the corresponding instance score to obtain the instance segmentation result;
  • the processing module 443 is further configured to use the corresponding target object detection frame to perform truncation processing on the instance segmentation result;
  • the calculation module 444 is used to calculate the error between the instance segmentation result after the truncation process and the corresponding label, and at the same time calculate the error between the detection frame and the corresponding target object detection frame;
  • the calculation module 444 is further configured to calculate the total loss value according to the error between the truncated instance segmentation result and the corresponding label, and the error between the detection frame and the corresponding target object detection frame;
  • the judgment module 445 is used to judge the total loss value. When the total loss value is less than the second preset value, stop training the deep learning model and determine the corresponding deep learning model when the total loss value is less than the second preset value Split the model for the instance.
  • the aforementioned prediction module 442 is specifically configured to:
  • the above prediction module 442 is also used to:
  • each generated detection frame is subjected to maximum pooling processing and the detection frame after the maximum pooling processing that meets the first preset condition is stored.
  • the above-mentioned training module 44 is also used to train the deep learning model according to the learning rate corresponding to the current total loss value.
  • pruning module 41 is specifically used for:
  • the influence factor corresponding to the network layer to be pruned in the deep learning model is the scaling factor obtained after the normalization calculation of the network layer to be pruned;
  • the network layer corresponding to the impact factor is pruned.
  • the training device of the instance segmentation model provided in this embodiment only the division of the above functional modules is used as an example. In practical applications, the above function allocation can be completed by different functional modules as needed, i.e. The internal structure of the device is divided into different functional modules to complete all or part of the functions described above.
  • the training device of the instance segmentation model of this embodiment belongs to the same concept as the training method embodiment of the instance segmentation model in the above-mentioned embodiment 1. For its specific implementation process and beneficial effects, please refer to the text recognition model training method embodiment. Repeat it again.

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Abstract

La présente demande concerne un procédé et un appareil de formation d'un modèle de segmentation d'instance, et un procédé de segmentation d'instance. Le procédé de formation d'un modèle de segmentation d'instance consiste : à élaguer un modèle d'apprentissage profond pré-construit; à acquérir un ensemble de formation et à marquer ce dernier, l'ensemble de formation étant un ensemble d'images RGBD, qui sont recueillies par des appareils de prise de vues de profondeur différentes et comportent des objets cibles dans un scénario, et les images RGBD comprenant des images de profondeur et des images couleur; et à former le modèle d'apprentissage profond élagué à l'aide de l'ensemble de formation marqué, de façon à obtenir un modèle de segmentation d'instance. Selon la présente demande, une structure de réseau d'un modèle de segmentation d'instance existant est élaguée, de telle sorte que l'ensemble du modèle est plus léger, ce qui permet d'augmenter la vitesse de formation de modèle et la vitesse de prédiction de modèle; de plus, afin d'empêcher une réduction de la précision de prédiction de modèle provoquée par une réduction des couches de réseau, des images de profondeur sont ajoutées, de telle sorte que le nombre de canaux soit augmenté, et la précision de formation de modèle et la précision de prédiction de modèle soient améliorées.
PCT/CN2021/095363 2020-05-26 2021-05-24 Procédé et appareil de formation d'un modèle de segmentation d'instance, et procédé de segmentation d'instance WO2021238826A1 (fr)

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WO2023155581A1 (fr) * 2022-02-21 2023-08-24 京东鲲鹏(江苏)科技有限公司 Procédé et appareil de détection d'image
CN114612825A (zh) * 2022-03-09 2022-06-10 云南大学 基于边缘设备的目标检测方法
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